The era of stitching together isolated models through fragile REST endpoints is coming to an end. Enterprise AI architectures are shifting from siloed single-modality tasks — separate systems for copy generation, text-to-speech, visual synthesis, and video rendering — toward unified, end-to-end multimodal pipelines. Where legacy architectures created significant latency and contextual fragmentation between modalities, modern enterprise pipelines transform a single textual prompt or brand brief into synchronized video, human-grade voiceover, and custom visual assets in a single execution flow. The architectural decision that determines whether this is achievable — Native End-to-End Multimodal Models versus Compound Orchestrated Pipelines — is the foundational choice that shapes every downstream engineering decision. For high-fidelity enterprise video, localized voice synthesis, and strict brand adherence, Compound AI Systems configured as Directed Acyclic Graphs remain the industry standard — delivering the granular quality control, deterministic schema validation, and modular component swapping that native multimodal models cannot yet match at production scale.